Neural Network Based Epileptic EEG Detection and Classification

TL;DR

This paper presents a neural network model that effectively classifies EEG signals for epileptic seizure detection, outperforming existing methods and aiding neurosurgeons in diagnosis and surgical planning.

Contribution

The study introduces a novel textual one-dimensional vector representation of EEG signals and achieves state-of-the-art classification accuracy on the Bonn dataset.

Findings

Achieved 81% sensitivity and specificity for five-class EEG classification.

Achieved 99.9% sensitivity and 99.5% specificity for binary classification.

Model outperforms existing 2D models in EEG classification accuracy.

Abstract

Timely diagnosis is important for saving the life of epileptic patients. In past few years, a lot of treatments are available for epilepsy. These treatments require use of anti-seizure drugs but are not effective in controlling frequency of seizure. There is need of removal of an affected region using surgery. Electroencephalogram (EEG) is a widely used technique for monitoring the brain activity and widely popular for seizure region detection. It is used before surgery for locating affected region. This manual process, using EEG graphs, is time consuming and requires deep expertise. In the present paper, a model has been proposed that preserves the true nature of an EEG signal in form of textual one-dimensional vector. The proposed model achieves a state of art performance for Bonn University dataset giving an average sensitivity, specificity of 81% and 81.4% respectively for classification of EEG data among all five classes. Also for binary classification achieving 99.9%, 99.5% score value for specificity and sensitivity instead of 2D models used by other researchers. Thus, developed system will significantly help neurosurgeons in the increase of their performance.